Classical regression analysis uses partial coefficients to measure the influences of some variables (regressors) on another variable (regressand). However, a descriptive point of view shows that these coefficients are very bad measures of influence. Their interpretation as an average change of the regressand is only valid if the regressors are weakly correlated, and they are useless when the de...

This paper demonstrates that measurement error can conspire with multicollinearity among explanatory variables to mislead an investigator. A causal variable measured with error may be overlooked and its significance transferred to a surrogate. The latter’s significance can then be entirely spurious, in that controlling it will not predictably change the response variable. In epidemiological res...

When data from different networks are merged for mapping purpose, biases may appear and lead to unrealistic results. In this paper we define a geostatistical model that generalizes the universal kriging model such that it can handle heterogeneous data. Multiple bias sources can be treated simultaneously through the notion of bias factors. The associated best linear unbiased estimation and predi...

the latest known source of multicollinearity, a nonorthogonality of two or more explanatory variables in multiple regression models, is high leverage points. Interpreting a fitted regression model may become impossible by the influential impacts of multicollinearity. In this paper, we attempt to investigate the impact of different sample sizes as one of the main causing factors of high leverage...

The binary logistic regression model (LRM) is practical in situations when the response variable (RV) dichotomous. maximum likelihood estimator (MLE) generally considered to estimate LRM parameters. However, presence of multicollinearity (MC), MLE not correct choice due its inflated standard deviation (SD) and errors (SE) estimates. To combat MC, commonly used biased estimators, i.e., Ridge est...

Assessing the harmful effects of multicollinearity in a regression model with multiple predictors has always been one of the great problems in applied econometrics. As correlations amongst predictors are almost always present to some extent (especially in time-series data generated by natural experiments), the question is at what point does inter-correlation become harmful. Despite receiving qu...

In presence of multicollinearity principal component regression (PCR) is sometimes suggested for the estimation of the regression coefficients of a multiple regression model. Due to ambiguities in the interpretation involved by the orthogonal transformation of the set of explanatory variables the method could not yet gain wide acceptance. Factor analysis regression (FAR) provides a model-based ...

In multiple regression, different techniques are available to deal with the situation where predictors large in number, and multicollinearity exists among them. Some of these approaches rely on correlation others depend principal components. To cope influential observations (outliers, leverage, or both) data matrix for regression purposes, two proposed this paper. These Robust Correlation Based...

This study aims to determine (1) the effect of price on purchasing decisions at traditional restaurants in Sumbawa (2) quality (3) location Bengawan, Raberas Resto, Sidodadi Sumbawa. research includes associative quantitative research. The population this were all consumers food sampling method used criteria that are who over 17 years old. type data is primary obtained from results filling out ...